Massive Random Access of Machine-to-Machine Communications in LTE Networks: Modeling and Throughput Optimization

A key challenge for enabling machine-to-machine (M2M) communications in long-term evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of the M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each machine-type device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the access class barring (ACB) factor and the uniform backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals.